What Vector database means in practice
A vector database stores high-dimensional vectors (embeddings) and serves nearest-neighbour queries with sub-100ms latency at enterprise scale. For sovereign deployments MindMap uses pgvector under 10M chunks (operational simplicity — one fewer system to back up), Qdrant for 10–100M chunks (faster snapshot/restore, better payload filtering), and Milvus beyond 100M chunks or where GPU-accelerated indexing matters. We deliberately avoid Pinecone, Weaviate Cloud and Chroma Cloud — they don't ship as on-prem and therefore don't meet the sovereign requirement. The choice between the three open-source options is operational not architectural; pick on operability not micro-benchmark performance.
Related terms
Embedding →
A dense numeric vector representation of a piece of text (or image, or audio) such that semantically similar inputs produce vectors close in the embedding space.
Retrieval-Augmented Generation (RAG) →
A pattern where, instead of relying solely on the LLM's training, the system retrieves relevant documents from a knowledge base and includes them in the prompt as context.
Hybrid retrieval →
Combining dense vector search with sparse keyword search (BM25), then fusing the results — typically yielding 15–25% accuracy lift over either alone.
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The Sovereign AI Playbook.
Enterprise generative AI that runs on your servers.
- 01Definition. The four non-negotiable properties of a sovereign deployment — and the three patterns that look sovereign but aren't.
- 02Why now. Three forces that converged in 2026: regulator pressure, the open-weights capability gap closing, and unit economics flipping.
- 03Reference architecture. The five-layer stack we deploy: applications → agent runtime → sovereign LLM → RAG + KB → data stores. With model and tooling choices for each.
- 04The regulators. SAMA · RBI · UK ICO · EU AI Act · DPDP · HIPAA — what each requires and how it maps to architecture.
All 62 terms, in plain language
Sovereign AI, RAG, agentic AI, IDP, MLOps and the regulations that shape enterprise AI.